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#' Identification of profiles, weights, majority threshold and veto and
#' dictator thresholds for the MRSort sorting approach extended to handle large
#' performance differences.
#'
#' MRSort is a simplified ElectreTRI method that uses the pessimistic
#' assignment rule, without indifference or preference thresholds attached to
#' criteria. LPDMRSort considers both a binary discordance and a binary
#' concordance conditions including several interactions between them. The
#' identification of the profiles, weights, majority threshold and veto and
#' dictator thresholds are done by taking into account assignment examples.
#'
#'
#' @param performanceTable Matrix or data frame containing the performance
#' table. Each row corresponds to an alternative, and each column to a
#' criterion. Rows (resp. columns) must be named according to the IDs of the
#' alternatives (resp. criteria).
#' @param assignments Vector containing the assignments (IDs of the categories)
#' of the alternatives to the categories. The elements are named according to
#' the alternatives.
#' @param categoriesRanks Vector containing the ranks of the categories. The
#' elements are named according to the IDs of the categories.
#' @param criteriaMinMax Vector containing the preference direction on each of
#' the criteria. "min" (resp. "max") indicates that the criterion has to be
#' minimized (maximized). The elements are named according to the IDs of the
#' criteria.
#' @param majorityRule String denoting how the vetoes and dictators are
#' combined in order to form the assignment rule. The values to choose from
#' are "M", "V", "D", "v", "d", "dV", "Dv", "dv". "M" corresponds to using
#' only the majority rule without vetoes or dictators, "V" considers only the
#' vetoes, "D" only the dictators, "v" is like "V" only that a dictator may
#' invalidate a veto, "d" is like "D" only that a veto may invalidate a
#' dictator, "dV" is like "V" only that if there is no veto we may then
#' consider the dictator, "Dv" is like "D" only that when there is no dictator
#' we may consider the vetoes, while finally "dv" is identical to using both
#' dictator and vetoes only that when both are active they invalidate each
#' other, so the majority rule is considered in that case.
#' @param readableWeights Boolean parameter indicating whether the weights are
#' to be spaced more evenly or not.
#' @param readableProfiles Boolean parameter indicating whether the profiles
#' are to be spaced more evenly or not.
#' @param minmaxLPD Boolean parameter indicating whether the veto thresholds
#' are to be minimized (or maximized if lower criteria values are preferred)
#' while the dictator thresholds are to be maximized (or minimized if lower
#' criteria values are preferred).
#' @param alternativesIDs Vector containing IDs of alternatives, according to
#' which the data should be filtered.
#' @param criteriaIDs Vector containing IDs of criteria, according to which the
#' data should be filtered.
#' @return The function returns a list structured as follows :
#' \item{lambda}{The majority threshold.} \item{weights}{A vector containing
#' the weights of the criteria. The elements are named according to the
#' criteria IDs.} \item{profilesPerformances}{A matrix containing the lower
#' profiles of the categories. The columns are named according to the
#' criteria, whereas the rows are named according to the categories. The lower
#' profile of the lower category can be considered as a dummy profile.}
#' \item{vetoPerformances}{A matrix containing the veto profiles of the
#' categories. The columns are named according to the criteria, whereas the
#' rows are named according to the categories. The veto profile of the lower
#' category can be considered as a dummy profile.} \item{solverStatus}{The
#' solver status as given by glpk.}
#' @references Bouyssou, D. and Marchant, T. An axiomatic approach to
#' noncompen- satory sorting methods in MCDM, II: more than two categories.
#' European Journal of Operational Research, 178(1): 246--276, 2007.
#'
#' Meyer, P. and Olteanu, A-L. Integrating large positive and negative
#' performance differences in majority-rule sorting models. European Journal
#' of Operational Research, submitted, 2015.
#' @keywords methods
#' @examples
#'
#' # the performance table
#'
#' performanceTable <- rbind(c(10,10,9), c(10,9,10), c(9,10,10), c(9,9,10),
#' c(9,10,9), c(10,9,9), c(10,10,7), c(10,7,10),
#' c(7,10,10), c(9,9,17), c(9,17,9), c(17,9,9),
#' c(7,10,17), c(10,17,7), c(17,7,10), c(7,17,10),
#' c(17,10,7), c(10,7,17), c(7,9,17), c(9,17,7),
#' c(17,7,9), c(7,17,9), c(17,9,7), c(9,7,17))
#'
#' rownames(performanceTable) <- c("a1", "a2", "a3", "a4", "a5", "a6", "a7",
#' "a8", "a9", "a10", "a11", "a12", "a13",
#' "a14", "a15", "a16", "a17", "a18", "a19",
#' "a20", "a21", "a22", "a23", "a24")
#'
#' colnames(performanceTable) <- c("c1","c2","c3")
#'
#' categoriesRanks <-c(1,2)
#'
#' names(categoriesRanks) <- c("P","F")
#'
#' criteriaMinMax <- c("max","max","max")
#'
#' names(criteriaMinMax) <- colnames(performanceTable)
#'
#' assignments <-rbind(c("P","P","P","F","F","F","F","F","F","F","F","F",
#' "F","F","F","F","F","F","F","F","F","F","F","F"),
#' c("P","P","P","F","F","F","P","P","P","P","P","P",
#' "P","P","P","P","P","P","P","P","P","P","P","P"),
#' c("P","P","P","F","F","F","F","F","F","F","F","F",
#' "P","P","P","P","P","P","F","F","F","F","F","F"),
#' c("P","P","P","F","F","F","P","P","P","P","P","P",
#' "P","P","P","P","P","P","F","F","F","F","F","F"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "F","F","F","F","F","F","F","F","F","F","F","F"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "P","P","P","P","P","P","P","P","P","P","P","P"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "P","P","P","P","P","P","F","F","F","F","F","F"))
#'
#' colnames(assignments) <- rownames(performanceTable)
#'
#' majorityRules <- c("V","D","v","d","dV","Dv","dv")
#'
#' for(i in 1:1)# change to 7 in order to perform all tests
#' {
#' x<-LPDMRSortInferenceExact(performanceTable, assignments[i,],
#' categoriesRanks, criteriaMinMax,
#' majorityRule = majorityRules[i],
#' readableWeights = TRUE,
#' readableProfiles = TRUE,
#' minmaxLPD = TRUE)
#'
#' ElectreAssignments<-LPDMRSort(performanceTable, x$profilesPerformances,
#' categoriesRanks,
#' x$weights, criteriaMinMax, x$lambda,
#' criteriaVetos=x$vetoPerformances,
#' criteriaDictators=x$dictatorPerformances,
#' majorityRule = majorityRules[i])
#'
#' print(x)
#'
#' print(all(ElectreAssignments == assignments[i,]))
#' }
#'
#' @export LPDMRSortInferenceExact
#' @import glpkAPI
LPDMRSortInferenceExact <- function(performanceTable, assignments, categoriesRanks, criteriaMinMax, majorityRule = "M", readableWeights = FALSE, readableProfiles = FALSE, minmaxLPD = FALSE, alternativesIDs = NULL, criteriaIDs = NULL){
## check the input data
if (!((is.matrix(performanceTable) || (is.data.frame(performanceTable)))))
stop("wrong performanceTable, should be a matrix or a data frame")
if (!(is.vector(assignments)))
stop("assignments should be a vector")
if (!(is.vector(categoriesRanks)))
stop("categoriesRanks should be a vector")
if (!(is.vector(criteriaMinMax)))
stop("criteriaMinMax should be a vector")
if (!is.character(majorityRule))
stop("majorityRule should be a character or a string of characters")
else if (!(majorityRule %in% c("M","V","D","v","d","dV","Dv","dv")))
stop("majorityRule needs to take values in {'M','V','D','v','d','dV','Dv','dv'}")
if (!is.logical(readableWeights))
stop("readableWeights should be a boolean")
if (!is.logical(readableProfiles))
stop("readableProfiles should be a boolean")
if (!is.logical(minmaxLPD))
stop("minmaxLPD should be a boolean")
if (!(is.null(alternativesIDs) || is.vector(alternativesIDs)))
stop("alternativesIDs should be a vector")
if (!(is.null(criteriaIDs) || is.vector(criteriaIDs)))
stop("criteriaIDs should be a vector")
## filter the data according to the given alternatives and criteria
if (!is.null(alternativesIDs)){
performanceTable <- performanceTable[alternativesIDs,]
assignments <- assignments[alternativesIDs]
}
if (!is.null(criteriaIDs)){
performanceTable <- performanceTable[,criteriaIDs]
criteriaMinMax <- criteriaMinMax[criteriaIDs]
}
# data is filtered, check for some data consistency
# if there are less than 2 criteria or 2 alternatives, there is no MCDA problem
if (is.null(dim(performanceTable)))
stop("less than 2 criteria or 2 alternatives")
# -------------------------------------------------------
numCrit <- dim(performanceTable)[2]
numAlt <- dim(performanceTable)[1]
numCat <- length(categoriesRanks)
tempPath <- tempdir()
# get model file depending on function options
modelfilename <- paste("MRSort", c("","V","D","DV1","DV2","DV3","DV4","DV5")[match(majorityRule,c("M","V","D","v","d","dV","Dv","dv"))], "InferenceModel", sep = "")
if(readableWeights || readableProfiles)
{
modelfilename <- paste(modelfilename, "Spread", sep = "")
if(readableWeights)
modelfilename <- paste(modelfilename, "Weights", sep = "")
if(readableProfiles)
modelfilename <- paste(modelfilename, "Profiles", sep = "")
}
if(minmaxLPD & majorityRule != "")
modelfilename <- paste(modelfilename, "LPD", sep = "")
modelfilename <- paste(modelfilename, ".gmpl", sep = "")
modelFile <- system.file("extdata", modelfilename, package="MCDA")
dataFile <- tempfile()
file.copy(modelFile, dataFile)
sink(dataFile, append=TRUE)
cat("data;\n")
cat("param X := ")
cat(numAlt)
cat(";\n\n")
cat("param F := ")
cat(numCrit)
cat(";\n\n")
cat("param Fdir := \n")
for (i in 1:numCrit){
cat(i)
cat("\t")
if (criteriaMinMax[i]=="min")
cat("-1")
else
cat("1")
if (i!=numCrit)
cat("\n")
else
cat(";\n\n")
}
cat("param Fmin :=\n")
for (i in 1:numCrit){
cat(i)
cat("\t")
cat(apply(performanceTable, 2, min)[i])
if (i!=numCrit)
cat("\n")
else
cat(";\n\n")
}
cat("param Fmax :=\n")
for (i in 1:numCrit){
cat(i)
cat("\t")
cat(apply(performanceTable, 2, max)[i])
if (i!=numCrit)
cat("\n")
else
cat(";\n\n")
}
cat("param K := ")
cat(numCat)
cat(";\n\n")
cat("param A:=\n")
for (i in 1:numAlt){
cat(i)
cat("\t")
cat(categoriesRanks[assignments[i]])
if (i!=numAlt)
cat("\n")
else
cat(";\n\n")
}
cat("param PTx : ")
cat(1:numCrit)
cat(" := \n")
for (i in 1:numAlt){
cat(i)
cat("\t")
cat(performanceTable[i,])
if (i!=numAlt)
cat("\n")
else
cat(";\n\n")
}
cat("param gamma:=0.001;\n")
cat("end;\n")
sink()
lp<-glpkAPI::initProbGLPK()
tran<-glpkAPI::mplAllocWkspGLPK()
glpkAPI::setMIPParmGLPK(PRESOLVE, GLP_ON)
glpkAPI::termOutGLPK(GLP_OFF)
out<-glpkAPI::mplReadModelGLPK(tran, dataFile, skip=0)
if (is.null(out))
out <- glpkAPI::mplGenerateGLPK(tran)
else
stop(glpkAPI::return_codeGLPK(out))
if (is.null(out))
glpkAPI::mplBuildProbGLPK(tran,lp)
else
stop(glpkAPI::return_codeGLPK(out))
glpkAPI::solveMIPGLPK(lp)
solverStatus <- paste("Failed (",glpkAPI::return_codeGLPK(glpkAPI::mipStatusGLPK(lp)),")")
error <- TRUE
if(glpkAPI::mipStatusGLPK(lp)==5){
solverStatus <- 'Solution found'
glpkAPI::mplPostsolveGLPK(tran, lp, sol = GLP_MIP)
solution <- glpkAPI::mipColsValGLPK(lp)
varnames <- c()
for (i in 1:length(solution))
varnames <- c(varnames,glpkAPI::getColNameGLPK(lp,i))
paro <- "["
parc <- "]"
error <- FALSE
}
if (!error){
lambda <- solution[varnames=="lambda"]
weightsnames <- c()
for (i in 1:numCrit)
{
weightsnames <- c(weightsnames,paste("w",paro,i,parc,sep=""))
}
weights <- c()
for (i in 1:numCrit)
weights <- c(weights,solution[varnames==weightsnames[i]])
names(weights) <- colnames(performanceTable)
ptknames <- matrix(nrow=numCat,ncol=numCrit)
for (i in 2:(numCat+1)){
for (j in 1:numCrit)
{
ptknames[i-1,j] <- paste("PTk",paro,i,",",j,parc,sep="")
}
}
profilesPerformances <- matrix(rep(NA,numCat*numCrit),nrow=numCat,ncol=numCrit)
# the last profile (bottom one) doesn't do anything so we keep it NA
for (i in 1:(numCat-1)){
for (j in 1:numCrit)
profilesPerformances[i,j] <- solution[varnames==ptknames[i,j]]
}
rownames(profilesPerformances) <- names(categoriesRanks)
colnames(profilesPerformances) <- colnames(performanceTable)
vetoPerformances <- NULL
if(majorityRule %in% c("V","v","d","dV","Dv","dv"))
{
ptvnames <- matrix(nrow=numCat,ncol=numCrit)
for (i in 2:(numCat+1)){
for (j in 1:numCrit)
{
ptvnames[i-1,j] <- paste("PTv",paro,i,",",j,parc,sep="")
}
}
vetoPerformances <- matrix(rep(NA,numCat*numCrit),nrow=numCat,ncol=numCrit)
# bottom profile doesn't do anything, keep it as NA
for (i in 1:(numCat-1)){
for (j in 1:numCrit)
vetoPerformances[i,j] <- solution[varnames==ptvnames[i,j]]
}
rownames(vetoPerformances) <- names(categoriesRanks)
colnames(vetoPerformances) <- colnames(performanceTable)
}
dictatorPerformances <- NULL
if(majorityRule %in% c("D","v","d","dV","Dv","dv"))
{
ptdnames <- matrix(nrow=numCat,ncol=numCrit)
for (i in 2:(numCat+1)){
for (j in 1:numCrit)
{
ptdnames[i-1,j] <- paste("PTd",paro,i,",",j,parc,sep="")
}
}
dictatorPerformances <- matrix(rep(NA,numCat*numCrit),nrow=numCat,ncol=numCrit)
# bottom profile doesn't do anything, keep it as NA
for (i in 1:(numCat-1)){
for (j in 1:numCrit)
dictatorPerformances[i,j] <- solution[varnames==ptdnames[i,j]]
}
rownames(dictatorPerformances) <- names(categoriesRanks)
colnames(dictatorPerformances) <- colnames(performanceTable)
}
if(majorityRule %in% c("V","v","d","dV","Dv","dv"))
{
# determine which vetoes are actually used and remove those that are simply an artefact of the linear program
used <- LPDMRSortIdentifyUsedVetoProfiles(performanceTable, assignments, sort(categoriesRanks), criteriaMinMax, lambda, weights, profilesPerformances, vetoPerformances, dictatorPerformances, majorityRule, alternativesIDs, criteriaIDs)
for (k in (numCat-1):1)
{
cat <- names(categoriesRanks)[categoriesRanks == k]
for (j in 1:numCrit)
{
if (!used[cat,j])
vetoPerformances[cat,j] <- NA
}
}
}
if(majorityRule %in% c("D","v","d","dV","Dv","dv"))
{
# determine which dictators are actually used and remove those that are simply an artefact of the linear program
used <- LPDMRSortIdentifyUsedDictatorProfiles(performanceTable, assignments, sort(categoriesRanks), criteriaMinMax, lambda, weights, profilesPerformances, dictatorPerformances, vetoPerformances, majorityRule, alternativesIDs, criteriaIDs)
for (k in (numCat-1):1)
{
cat <- names(categoriesRanks)[categoriesRanks == k]
for (j in 1:numCrit)
{
if (!used[cat,j])
dictatorPerformances[cat,j] <- NA
}
}
}
return(list(lambda = lambda, weights = weights, profilesPerformances = profilesPerformances, vetoPerformances = vetoPerformances, dictatorPerformances = dictatorPerformances, solverStatus = solverStatus))
}
else
{
return(list(solverStatus = solverStatus))
}
}
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